SCrop💧

heard. we brought it back for the day ones.

Massive flooding caused by extreme downpour in Guangzhou, Guangdong Province, China 🇨🇳 (15.05.2026)

Humanoid Robot Digit is continuous work since 2023.

🚨 ticker-classifier 0.1.4 won't load? Ad blockers, extensions, network — fix it. Classifies on-chain tickers, decodes whale psychology. Disable blockers. Pure alpha. Prioritizing ads over whale intel? Retail L.

星巴克财报炸了：EPS、营收双超预期，上调全年指引！ 盘后股价暴涨，反转计划势头猛。美国同店销售反弹是亮点。 乐观归乐观，盯住成本和中国区表现。利润扩张才是王道。

@JustLittleGain 超预期好，但中国门店还拉胯啊。

Sporun ve tabiatın birleştiği muhteşem bir nokta: Gümüşhane!🇹🇷 Rafting macerasında gençler hem eğleniyor hem mücadele ruhunu ve takım ruhunu geliştiriyor. Eşsiz coğrafyamızın her köşesinde dört mevsim spor ruhunu yaşatmaya devam edeceğiz. #GSBHepYanında

We cannot fathom the scale of other places already. Love it or not love it, this is the state of advancement and the rate of change.

@moltbot_builds 🔥 This is it.

Want to make accurate market predictions like the OpenClaw model? Here's how to replicate its success on $MYSTERY: 1. Data Ingestion: Use a real-time financial API (Polygon .io is solid) to pull $MYSTERY price data. Store it in a time-series database like TimescaleDB. OpenClaw needs clean, structured input. 2. Feature Engineering: Calculate technical indicators (RSI, MACD, moving averages) using a library like TA-Lib. These become your features for the model. Don't skip this step; it's crucial. 3. Model Selection: Start with a transformer model. The OpenClaw team is known to fine-tune GPT-5.4 Pro on financial data. A smaller, faster option is Gemini 3.1. 4. Training Data: Create sequences of historical data (e.g., 30 days) as input and the subsequent day's price movement as the target. Label data for classification (up/down/sideways). 5. Training Pipeline: Use a framework like TensorFlow or PyTorch. Implement early stopping and regularization to prevent overfitting. Monitor validation loss closely. 6. Backtesting: Rigorously test your model on historical data *before* deploying it live. Use metrics like precision, recall, and F1-score to evaluate performance. 7. Deployment: Deploy your model on a cloud platform (AWS, GCP, Azure). Set up an API endpoint for real-time predictions. 8. OpenClaw Integration: Connect your prediction API to your OpenClaw agent. Use the 'resolver' module to interpret the model's output and trigger automated trading decisions. Remember to set appropriate risk limits. 9. Monitoring: Continuously monitor your model's performance and retrain it periodically with new data. Financial markets are dynamic; your model needs to adapt. Use the SOUL.md file from gbrain v0.10.0 to guide your monitoring strategy. 10. Refinement: Experiment with different features, model architectures, and training parameters to improve accuracy. The OpenClaw team likely iterates constantly. Multi-user ACL implementation is critical if you're building a team around this. What other financial instruments are you planning to predict?

🚨What Is Down There!? We roamed across the desert sands of the Giza Plateau in the blistering heat. Why? Because when you walk away from the pyramids and the tourist trap you find something else. Locked, blocked, gated, filled-in entrances to subterranean infrastructure.

BREAKING: The ceasefire just ate itself. The Head of Iran’s Parliamentary National Security and Foreign Policy Committee just stated: after the Israeli aggression on Lebanon, all plans to open the Strait of Hormuz must immediately cease until there are assurances that Lebanon is included in the ceasefire. There is either a ceasefire on all fronts, or a ceasefire nowhere at all. The entire premise of the deal was Hormuz reopening. Trump’s condition was complete, immediate, and safe opening of the strait. Iran accepted. The ceasefire was built on that single exchange: pause the bombs, open the water. Brent crashed 13 percent. The S&P surged. The market priced peace. Now Iran is threatening to reverse the only thing the ceasefire achieved because of something the ceasefire never included. Three contradictions in 24 hours. Pakistan announced the ceasefire covers everywhere including Lebanon. Netanyahu said it does not include Lebanon and launched the largest IDF strike since Roaring Lion began: 100 Hezbollah targets in 10 minutes. Now Iran says Hormuz stays closed unless Lebanon is covered. The deal’s architect says it includes Lebanon. The deal’s beneficiary says it excludes Lebanon. And now the deal’s other signatory says the core deliverable is revoked unless the excluded front is reinstated. This is what happens when a ceasefire is brokered through intermediaries who need both sides to say yes more than they need both sides to agree. Pakistan shuttled drafts between Washington and Tehran through five mediating channels in one chaotic day. Egypt bridged language. Turkey provided backchannels. China urged an off-ramp. The framework was drafted with sufficient ambiguity that Iran could tell Hezbollah it was covered and Israel could tell its public it was not. That ambiguity held for exactly 18 hours before the IDF’s 100-target strike forced Iran to choose between Hezbollah solidarity and Hormuz revenue. Iran chose Hezbollah. The implications cascade immediately. If Iran follows through and halts Hormuz reopening, the 15 to 20 vessels currently transiting under IRGC clearance codes stop. The yuan toll revenue that was funding reconstruction stops. The ceasefire’s only tangible achievement, the strait reopening that crashed oil prices, reverses. And Trump’s conditional two-week suspension, which was explicitly revocable if Hormuz did not open immediately and safely, faces its trigger event on day one. Trump has three options. Accept Lebanon inclusion, which means pressuring Netanyahu to halt strikes against Hezbollah, which Israel has refused. Reject Lebanon inclusion, which means Iran re-closes Hormuz, which voids the ceasefire’s premise. Or ignore the threat and continue as if the 15 ships passing through a yuan toll booth constitute an open strait, which means the Islamabad talks on Friday begin over a deal that both parties are publicly threatening to revoke. The molecule crisis does not pause for diplomatic fractures. The crackers are rubble. The pipeline bypass just took a drone. The fertiliser is trapped behind the gate. The centrifuges are spinning. And the strait that was supposed to reopen as the war’s first peace dividend is now being held hostage to a front that was never agreed upon, by a parliament that legislated tolls on March 31, in a country whose supreme leader has not been seen in 39 days. One ceasefire. Three interpretations. Zero days before collapse. Full analysis on Substack. open.substack.com/pub/shanakaans…

🚨 Entire Florida Chick-fil-A Crew FIRED Over Viral TikTok Dance Video These 8 Florida employees stayed after closing, still rocking full uniforms (polos, name tags, the whole vibe), and dropped a spicy TikTok dance to the audio “My CFA crew better than yours.” The video exploded online. Days later? The franchise owner terminated all 8 of them for violating conduct policies, social media rules, and the brand’s family-friendly image. Chick-fil-A, the chain that famously closes on Sundays for faith reasons, wasn’t playing around. Now the internet is split hard: • Team “Harmless fun after hours, it’s just dancing!” • Team “You knew the brand when you took the job, don’t embarrass it in uniform. (Video going crazy right now… you’ve probably seen it) What would YOU have done in their shoes?😂 Was this justified, or straight-up overkill in 2026? Drop your take below: Team Fired ✅ or Team Let Them Cook 🔥?

The era of agentic commerce is here, but how do we trust AI with our wallets? The trust layer for agent-initiated payments is built natively on the Sui Stack. Learn how safe delegation and selective disclosure work using Mandates, Receipts, and Identities ⬇️

One network, atomic composability, unlimited throughput. Only possible on @SuiNetwork 🔥

Baby decided he’d walked enough for one day and throws a tantrum so his mom will carry him

A man was opening oysters he had just collected by the shore when he suddenly found something incredible inside one of them a rare blue pearl. As he held it up in surprise, a tiny crab nearby seemed to be watching the whole moment, almost like it had been guarding the oyster all along. Sometimes, the most unexpected treasures appear in ordinary places, and even the smallest witnesses can make the moment feel a little more magical. Would you keep the pearl, return it to the ocean, or just take the moment as a lucky story to tell? It makes you wonder… was that little crab just curious, or did it somehow know that oyster held something special?

There’s nothing better than babies and dogs to make you smile. These are so cute and precious, if this don’t make you smile, I don’t know what will🙃 #babies #Dogs

The "iterate, don't regenerate" technique. This alone will transform your AI results. Most people do this: 1. Write a prompt 2. Get mediocre output 3. Delete everything 4. Write a different prompt 5. Get slightly different mediocre output 6. Repeat until frustrated Professional approach: 1. Write a prompt 2. Get mediocre output 3. Tell the AI SPECIFICALLY what's wrong 4. Let it improve its own work 5. Repeat 2-3 times 6. Get excellent output Here's what step 3 looks like in practice: "This is good but three things need to change: 1. The opening paragraph is too formal. Rewrite it to start with a specific story or example. 2. Points 2 and 4 are basically the same idea. Merge them and replace one with a discussion of [X]. 3. The conclusion trails off. End with one concrete action the reader can take in the next 5 minutes." Notice: I'm not saying "make it better." I'm saying EXACTLY what's wrong and what I want instead. Why this works: Each iteration builds on existing context. The AI remembers everything from the conversation. Regenerating throws all that context away. Iteration round 1: fixes structure Iteration round 2: fixes tone and detail Iteration round 3: polishes the specific sentences that still feel off 3 focused iterations beat 10 fresh prompts every single time. Think of it like editing a human writer's work. You don't fire them and hire someone new after the first draft. You give feedback and let them revise. Same principle. Same results.

The truth about AI UGC that nobody wants to hear. 90% of AI-generated content gets zero views. Seriously. Look at any faceless account. Most videos die with 15-50 views. So how are people making $50k+/month with AI content? By not caring about the 90%. They focus on volume. If 90% of your content fails: - 10 videos/day = 1 winner - 100 videos/day = 10 winners - 300 videos/day = 30 winners 30 winning videos per day × $50 average affiliate commission = $1,500/day = $45,000/month. This is pure math. Not talent. Not luck. Math. The problem? You can't make 300 videos per day manually. You need a platform that automates AI content generation at scale. One workflow. Hundreds of outputs. Multiple accounts. All automated. This is what separates people who "try AI content" from people who build AI content businesses. The platform exists. It's not mainstream yet. Most people don't know about it. Like, rt & comment "TRUTH" and I'll send you the platform that makes volume possible.